import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import distance
from scipy.interpolate import interp1d
from matplotlib.offsetbox import OffsetImage, AnnotationBbox

df_deepM_03 = pd.read_csv('../Data/KeypointsBereinigtNichtKompensiert_03.csv', sep=',', encoding='utf-8')
df_deepM_03.head(5)

print('Dimension:', df_deepM_03.shape)
print('Number of rows:', df_deepM_03.shape[0])
print('Number of columns:', df_deepM_03.shape[1])

Dimension: (937, 102)
Number of rows: 937
Number of columns: 102

# Variablen initiieren
frame_count = 0
frames_until_reset = []

# Iterieren über Dataframe, um Frames mit '1' zu finden
for index, row in df_deepM_03.iterrows():
    frame_count += 1
    
    if row["frame"] == 1 and frame_count > 1:
        frames_until_reset.append(frame_count -1)

if frame_count > 0:
    frames_until_reset.append(frame_count)

print("Number of frames until reset for each cycle:", frames_until_reset)

Number of frames until reset for each cycle: [182, 373, 565, 740, 937]

# Einzelne Bewegungen werden in verschiedene Dataframes gepackt
dfs = []
start = 0
for end in frames_until_reset:
    dfs.append(df_deepM_03.iloc[start:end])
    start = end

print("Number of splits:", len(dfs))

Number of splits: 5

plt.figure(figsize=(12, 6))
for i, df in enumerate(dfs):
    plt.scatter(df["frame"], df["y_16"], marker='o', color=f'C{i}', s=1)
    plt.plot(df["frame"], df["y_16"], color=f'C{i}', linestyle='-', linewidth=0.5, label=f'Datenpunkte Animation {i}')

plt.title("Rechte Hand (y_16) Bewegung 03")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

means03 = []
mean_values_03 = df_deepM_03[['frame', 'x_0', 'y_0', 'z_0', 'x_1', 'y_1', 'z_1', 'x_2', 'y_2', 'z_2', 'x_3', 'y_3', 'z_3',
                   'x_4', 'y_4', 'z_4', 'x_5', 'y_5', 'z_5', 'x_6', 'y_6', 'z_6', 'x_7', 'y_7', 'z_7',
                   'x_8', 'y_8', 'z_8', 'x_9', 'y_9', 'z_9', 'x_10', 'y_10', 'z_10', 'x_11', 'y_11', 'z_11',
                   'x_12', 'y_12', 'z_12', 'x_13', 'y_13', 'z_13', 'x_14', 'y_14', 'z_14', 'x_15', 'y_15', 'z_15',
                   'x_16', 'y_16', 'z_16', 'x_17', 'y_17', 'z_17', 'x_18', 'y_18', 'z_18', 'x_19', 'y_19', 'z_19',
                   'x_20', 'y_20', 'z_20', 'x_21', 'y_21', 'z_21', 'x_22', 'y_22', 'z_22', 'x_23', 'y_23', 'z_23',
                   'x_24', 'y_24', 'z_24', 'x_25', 'y_25', 'z_25', 'x_26', 'y_26', 'z_26', 'x_27', 'y_27', 'z_27',
                   'x_28', 'y_28', 'z_28', 'x_29', 'y_29', 'z_29', 'x_30', 'y_30', 'z_30', 'x_31', 'y_31', 'z_31',
                   'x_32', 'y_32', 'z_32']].groupby('frame').mean()
means03.append(mean_values_03)
mean_values_03.head(5)

plt.figure(figsize=(12, 6))

for i, df in enumerate(dfs):
    plt.scatter(df["frame"], df["y_16"], marker='o', color=f'C{i}', s=1)
    plt.plot(df["frame"], df["y_16"], color=f'C{i}', linestyle='-', linewidth=0.5, label=f'Datenpunkte Animation {i}')

plt.plot(mean_values_03.index, mean_values_03["y_16"], color='b', linestyle='-', linewidth=2, label='Mean Y_16')

plt.title("Rechte Hand (y_16) Bewegung 03 mit Mean")
plt.xlabel("Frame")
plt.legend(loc='best')
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.show()

df_deepM01 = pd.read_csv('../Data/KeypointsBereinigtKompensiert_01.csv', sep=',', encoding='utf-8')
df_deepM01.head(5)

print('Dimension:', df_deepM01.shape)
print('Number of rows:', df_deepM01.shape[0])
print('Number of columns:', df_deepM01.shape[1])

Dimension: (1096, 102)
Number of rows: 1096
Number of columns: 102

# Variablen initiieren
frame_count = 0
frames_until_reset = []

# Iterieren über Dataframe, um Frames mit '1' zu finden
for index, row in df_deepM01.iterrows():
    frame_count += 1
    
    if row["frame"] == 1 and frame_count > 1:
        frames_until_reset.append(frame_count -1)

if frame_count > 0:
    frames_until_reset.append(frame_count)

print("Number of frames until reset for each cycle:", frames_until_reset)

Number of frames until reset for each cycle: [210, 431, 652, 873, 1096]

# Einzelne Bewegungen werden in verschiedene Dataframes gepackt
dfsKomp01 = []
start = 0
for end in frames_until_reset:
    dfsKomp01.append(df_deepM01.iloc[start:end])
    start = end

print("Number of splits:", len(dfsKomp01))

Number of splits: 5

plt.figure(figsize=(12, 6)) 
for i, df in enumerate(dfsKomp01):
    plt.scatter(df["frame"], df["y_16"], marker='o', color=f'C{i}', s=1)
    plt.plot(df["frame"], df["y_16"], color=f'C{i}', linestyle='-', linewidth=0.5, label=f'Datenpunkte Animation {i}')

plt.title("Rechte Hand (y_16) Bewegung 01")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

means01 = []
mean_values_01 = df_deepM01[['frame', 'x_0', 'y_0', 'z_0', 'x_1', 'y_1', 'z_1', 'x_2', 'y_2', 'z_2', 'x_3', 'y_3', 'z_3',
                   'x_4', 'y_4', 'z_4', 'x_5', 'y_5', 'z_5', 'x_6', 'y_6', 'z_6', 'x_7', 'y_7', 'z_7',
                   'x_8', 'y_8', 'z_8', 'x_9', 'y_9', 'z_9', 'x_10', 'y_10', 'z_10', 'x_11', 'y_11', 'z_11',
                   'x_12', 'y_12', 'z_12', 'x_13', 'y_13', 'z_13', 'x_14', 'y_14', 'z_14', 'x_15', 'y_15', 'z_15',
                   'x_16', 'y_16', 'z_16', 'x_17', 'y_17', 'z_17', 'x_18', 'y_18', 'z_18', 'x_19', 'y_19', 'z_19',
                   'x_20', 'y_20', 'z_20', 'x_21', 'y_21', 'z_21', 'x_22', 'y_22', 'z_22', 'x_23', 'y_23', 'z_23',
                   'x_24', 'y_24', 'z_24', 'x_25', 'y_25', 'z_25', 'x_26', 'y_26', 'z_26', 'x_27', 'y_27', 'z_27',
                   'x_28', 'y_28', 'z_28', 'x_29', 'y_29', 'z_29', 'x_30', 'y_30', 'z_30', 'x_31', 'y_31', 'z_31',
                   'x_32', 'y_32', 'z_32']].groupby('frame').mean()
means01.append(mean_values_01)

mean_values_01.head(5)

plt.figure(figsize=(12, 6))

for i, df in enumerate(dfsKomp01):
    plt.scatter(df["frame"], df["y_16"], marker='o', color=f'C{i}', s=1)
    plt.plot(df["frame"], df["y_16"], color=f'C{i}', linestyle='-', linewidth=0.5, label=f'Datenpunkte Animation {i}')

plt.plot(mean_values_01.index, mean_values_01["y_16"], color='b', linestyle='-', linewidth=2, label='Mean Y_16')

plt.title("Rechte Hand (y_16) Bewegung 01 mit Mean")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

plt.figure(figsize=(12, 6)) 
for i, df in enumerate(dfsKomp01):
    plt.scatter(df["frame"], df["x_13"], marker='o', color=f'C{i}', s=1)
    plt.plot(df["frame"], df["x_13"], color=f'C{i}', linestyle='-', linewidth=0.5, label=f'Datenpunkte Animation {i}')

plt.title("Linker Ellbogen (x_13) Bewegung Video 01")
plt.xlabel("Frame")
plt.ylabel("Linker Ellbogen")
plt.grid(True)
plt.legend(loc='best')
plt.show()

plt.figure(figsize=(12, 6)) 
for i, df in enumerate(dfsKomp01):
    plt.scatter(df["frame"], df["y_15"], marker='o', color=f'C{i}', s=1)
    plt.plot(df["frame"], df["y_15"], color=f'C{i}', linestyle='-', linewidth=0.5, label=f'Datenpunkte Animation {i}')

plt.title("Linke Hand (y_15) Bewegung Video 01")
plt.xlabel("Frame")
plt.ylabel("Linke Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

df_deepM02 = pd.read_csv('../Data/KeypointsBereinigtKompensiert_02.csv', sep=',', encoding='utf-8')
df_deepM02.head(5)

print('Dimension:', df_deepM02.shape)
print('Number of rows:', df_deepM02.shape[0])
print('Number of columns:', df_deepM02.shape[1])

Dimension: (1715, 102)
Number of rows: 1715
Number of columns: 102

# Variablen initiieren
frame_count = 0
frames_until_reset = []

# Iterieren über Dataframe, um Frames mit '1' zu finden
for index, row in df_deepM02.iterrows():
    frame_count += 1

    if row["frame"] == 1 and frame_count > 1:
        frames_until_reset.append(frame_count -1)

if frame_count > 0:
    frames_until_reset.append(frame_count)

print("Number of frames until reset for each cycle:", frames_until_reset)

Number of frames until reset for each cycle: [337, 676, 1019, 1366, 1715]

# Einzelne Bewegungen werden in verschiedene Dataframes gepackt
dfsKomp02 = []
start = 0
for end in frames_until_reset:
    dfsKomp02.append(df_deepM02.iloc[start:end])
    start = end

print("Number of splits:", len(dfsKomp02))

Number of splits: 5

plt.figure(figsize=(12, 6)) 

for i, df in enumerate(dfsKomp02):

    plt.scatter(df["frame"], df["y_16"], marker='o', color=f'C{i}', s=1)
    plt.plot(df["frame"], df["y_16"], color=f'C{i}', linestyle='-', linewidth=0.5, label=f'Animation Datenpunkte {i}')

plt.title("Rechte Hand (y_16) Bewegung 03")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

means02 = []
mean_values_02 = df_deepM02[['frame', 'x_0', 'y_0', 'z_0', 'x_1', 'y_1', 'z_1', 'x_2', 'y_2', 'z_2', 'x_3', 'y_3', 'z_3',
                   'x_4', 'y_4', 'z_4', 'x_5', 'y_5', 'z_5', 'x_6', 'y_6', 'z_6', 'x_7', 'y_7', 'z_7',
                   'x_8', 'y_8', 'z_8', 'x_9', 'y_9', 'z_9', 'x_10', 'y_10', 'z_10', 'x_11', 'y_11', 'z_11',
                   'x_12', 'y_12', 'z_12', 'x_13', 'y_13', 'z_13', 'x_14', 'y_14', 'z_14', 'x_15', 'y_15', 'z_15',
                   'x_16', 'y_16', 'z_16', 'x_17', 'y_17', 'z_17', 'x_18', 'y_18', 'z_18', 'x_19', 'y_19', 'z_19',
                   'x_20', 'y_20', 'z_20', 'x_21', 'y_21', 'z_21', 'x_22', 'y_22', 'z_22', 'x_23', 'y_23', 'z_23',
                   'x_24', 'y_24', 'z_24', 'x_25', 'y_25', 'z_25', 'x_26', 'y_26', 'z_26', 'x_27', 'y_27', 'z_27',
                   'x_28', 'y_28', 'z_28', 'x_29', 'y_29', 'z_29', 'x_30', 'y_30', 'z_30', 'x_31', 'y_31', 'z_31',
                   'x_32', 'y_32', 'z_32']].groupby('frame').mean()
means02.append(mean_values_02)
    
mean_values_02.head(5)

plt.figure(figsize=(12, 6))

for i, df in enumerate(dfsKomp02):

    plt.scatter(df["frame"], df["y_16"], marker='o', color=f'C{i}', s=1)
    plt.plot(df["frame"], df["y_16"], color=f'C{i}', linestyle='-', linewidth=0.5, label=f'Datenpunkte Animation {i}')

plt.plot(mean_values_02.index, mean_values_02["y_16"], color='b', linestyle='-', linewidth=2, label='Mean Y_16')

plt.title("y_16 (rechte Hand) Bewegung DF02 mit Mean" )
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

df_MediaP = pd.read_csv('../Data/samples.csv', sep=',', encoding='utf-8')
df_MediaP.head(5)

print('Dimension:', df_MediaP.shape)
print('Number of rows:', df_MediaP.shape[0])
print('Number of columns:', df_MediaP.shape[1])

Dimension: (623, 102)
Number of rows: 623
Number of columns: 102

# Variablen initiieren
frame_count = 0
frames_until_reset = []

# Iterieren über Dataframe, um Frames mit '1' zu finden
for index, row in df_MediaP.iterrows():
    frame_count += 1
    
    if row["frame"] == 1 and frame_count > 1:
        frames_until_reset.append(frame_count -1)

if frame_count > 0:
    frames_until_reset.append(frame_count)

print("Number of frames until reset for each cycle:", frames_until_reset)

Number of frames until reset for each cycle: [172, 471, 623]

# DataFrame entsprechend splitten
df_MediaP1 = df_MediaP.iloc[:frames_until_reset[0]]
df_MediaP2 = df_MediaP.iloc[frames_until_reset[0]:frames_until_reset[1]]
df_MediaP3 = df_MediaP.iloc[frames_until_reset[1]:]

df_MediaP1.head(5)

print('Dimension MediaPipe02:', df_MediaP1.shape)

for i, df in enumerate(dfsKomp01):
    print(f"DataFrame {i+1} Shape:", df.shape)

Dimension MediaPipe02: (172, 102)
DataFrame 1 Shape: (210, 102)
DataFrame 2 Shape: (221, 102)
DataFrame 3 Shape: (221, 102)
DataFrame 4 Shape: (221, 102)
DataFrame 5 Shape: (223, 102)

df_MediaP2.head(5)

print('Dimension MediaPipe02:', df_MediaP2.shape)

for i, df in enumerate(dfsKomp02):
    print(f"DataFrame {i+1} Shape:", df.shape)

Dimension MediaPipe02: (299, 102)
DataFrame 1 Shape: (337, 102)
DataFrame 2 Shape: (339, 102)
DataFrame 3 Shape: (343, 102)
DataFrame 4 Shape: (347, 102)
DataFrame 5 Shape: (349, 102)

df_MediaP3.head(5)

print('Dimension MediaPipe03:', df_MediaP3.shape)

for i, df in enumerate(dfs):
    print(f"DataFrame {i+1} Shape:", df.shape)

Dimension MediaPipe03: (152, 102)
DataFrame 1 Shape: (182, 102)
DataFrame 2 Shape: (191, 102)
DataFrame 3 Shape: (192, 102)
DataFrame 4 Shape: (175, 102)
DataFrame 5 Shape: (197, 102)

plt.figure(figsize=(12, 6))

plt.scatter(df_MediaP1["frame"], df_MediaP1["z_16"], marker='o', color='gray', s=1)
plt.plot(df_MediaP1["frame"], df_MediaP1["z_16"], color='green', linestyle='-', linewidth=0.5, label='Line Y_16 Mediapipe 01')

plt.scatter(df_MediaP2["frame"], df_MediaP2["z_16"], marker='o', color='gray', s=1)
plt.plot(df_MediaP2["frame"], df_MediaP2["z_16"], color='blue', linestyle='-', linewidth=0.5, label='Line Y_16 Mediapipe 02')

plt.scatter(df_MediaP3["frame"], df_MediaP3["z_16"], marker='o', color='gray', s=1)
plt.plot(df_MediaP3["frame"], df_MediaP3["z_16"], color='red', linestyle='-', linewidth=0.5, label='Line Y_16 Mediapipe 03')


plt.title("Rechte Hand (z_16) Bewegung")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')

plt.show()

plt.figure(figsize=(12, 6)) 

plt.scatter(df_MediaP1["frame"], df_MediaP1["y_0"], marker='o', color='gray', s=1)
plt.plot(df_MediaP1["frame"], df_MediaP1["y_0"], color='green', linestyle='-', linewidth=0.5, label='Line Y_16 Mediapipe 01')

plt.scatter(df_MediaP2["frame"], df_MediaP2["y_0"], marker='o', color='gray', s=1)
plt.plot(df_MediaP2["frame"], df_MediaP2["y_0"], color='blue', linestyle='-', linewidth=0.5, label='Line Y_16 Mediapipe 02')

plt.scatter(df_MediaP3["frame"], df_MediaP3["y_0"], marker='o', color='gray', s=1)
plt.plot(df_MediaP3["frame"], df_MediaP3["y_0"], color='red', linestyle='-', linewidth=0.5, label='Line Y_16 Mediapipe 03')


plt.title("Kopf (y_0) Bewegung (nicken)")
plt.xlabel("Frame")
plt.ylabel("Kopf")
plt.grid(True)
# Move the legend outside of the plot
plt.legend(loc='best')

plt.show()

plt.figure(figsize=(12, 6))  

plt.scatter(df_MediaP1["frame"], df_MediaP1["y_16"], marker='o', color='gray', s=1)
plt.plot(df_MediaP1["frame"], df_MediaP1["y_16"], color='green', linestyle='-', linewidth=0.5, label='Line Y_16 Mediapipe 01')

plt.scatter(df_MediaP2["frame"], df_MediaP2["y_16"], marker='o', color='gray', s=1)
plt.plot(df_MediaP2["frame"], df_MediaP2["y_16"], color='blue', linestyle='-', linewidth=0.5, label='Line Y_16 Mediapipe 02')

plt.scatter(df_MediaP3["frame"], df_MediaP3["y_16"], marker='o', color='gray', s=1)
plt.plot(df_MediaP3["frame"], df_MediaP3["y_16"], color='red', linestyle='-', linewidth=0.5, label='Line Y_16 Mediapipe 03')


plt.title("y_16 (rechter Arm) Bewegungen")
plt.xlabel("Frame")
plt.ylabel("Rechter Arm")
plt.grid(True)
plt.legend(loc='best')
plt.show()

meansMP = []
df_MediaP12 = pd.concat([df_MediaP1, df_MediaP2])

mean_values_MP = df_MediaP12[['frame', 'x_0', 'y_0', 'z_0', 'x_1', 'y_1', 'z_1', 'x_2', 'y_2', 'z_2', 'x_3', 'y_3', 'z_3',
                   'x_4', 'y_4', 'z_4', 'x_5', 'y_5', 'z_5', 'x_6', 'y_6', 'z_6', 'x_7', 'y_7', 'z_7',
                   'x_8', 'y_8', 'z_8', 'x_9', 'y_9', 'z_9', 'x_10', 'y_10', 'z_10', 'x_11', 'y_11', 'z_11',
                   'x_12', 'y_12', 'z_12', 'x_13', 'y_13', 'z_13', 'x_14', 'y_14', 'z_14', 'x_15', 'y_15', 'z_15',
                   'x_16', 'y_16', 'z_16', 'x_17', 'y_17', 'z_17', 'x_18', 'y_18', 'z_18', 'x_19', 'y_19', 'z_19',
                   'x_20', 'y_20', 'z_20', 'x_21', 'y_21', 'z_21', 'x_22', 'y_22', 'z_22', 'x_23', 'y_23', 'z_23',
                   'x_24', 'y_24', 'z_24', 'x_25', 'y_25', 'z_25', 'x_26', 'y_26', 'z_26', 'x_27', 'y_27', 'z_27',
                   'x_28', 'y_28', 'z_28', 'x_29', 'y_29', 'z_29', 'x_30', 'y_30', 'z_30', 'x_31', 'y_31', 'z_31',
                   'x_32', 'y_32', 'z_32']].groupby('frame').mean()
meansMP.append(mean_values_MP)
    
mean_values_MP.head(5)

plt.figure(figsize=(12, 6))

plt.scatter(df_MediaP1["frame"], df_MediaP1["y_16"], marker='o', color='gray', s=1)
plt.plot(df_MediaP1["frame"], df_MediaP1["y_16"], color='green', linestyle='-', linewidth=0.5, label='Animation Y_16 Mediapipe 01')

plt.scatter(df_MediaP2["frame"], df_MediaP2["y_16"], marker='o', color='gray', s=1)
plt.plot(df_MediaP2["frame"], df_MediaP2["y_16"], color='blue', linestyle='-', linewidth=0.5, label='Animation Y_16 Mediapipe 02')

plt.scatter(df_MediaP3["frame"], df_MediaP3["y_16"], marker='o', color='gray', s=1)
plt.plot(df_MediaP3["frame"], df_MediaP3["y_16"], color='red', linestyle='-', linewidth=0.5, label='Animation Y_16 Mediapipe 03 (nicht kompensiert)')

plt.plot(mean_values_MP.index, mean_values_MP["y_16"], color='green', linestyle='-', linewidth=2, label='Mean Y_16 01 und 02 (kompensiert)')

plt.title("Rechte Hand (y_16) Bewegung DF01 mit Mean von DF 1 und 2")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

plt.figure(figsize=(12, 6))

plt.plot(mean_values_01.index, mean_values_01["y_16"], color='#054907', linestyle='-', linewidth=2, label='Mean Y_16 DeepMotion 01')
plt.plot(df_MediaP1["frame"], df_MediaP1["y_16"], color='green', linestyle='-', linewidth=2, label='Y_16 Mediapipe 01')

plt.plot(mean_values_02.index, mean_values_02["y_16"], color='darkblue', linestyle='-', linewidth=2, label='Mean Y_16 DeepMotion 02')
plt.plot(df_MediaP2["frame"], df_MediaP2["y_16"], color='blue', linestyle='-', linewidth=2, label='Y_16 Mediapipe 02')

plt.plot(mean_values_03.index, mean_values_03["y_16"], color='darkred', linestyle='-', linewidth=2, label='Mean Y_16 DeepMotion 03')
plt.plot(df_MediaP3["frame"], df_MediaP3["y_16"], color='red', linestyle='-', linewidth=2, label='Y_16 Mediapipe 03')

plt.title("Rechte Hand (y_16) Vergleich der Means")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

difference_in_mean_01 = df_MediaP1['y_16'].mean() - mean_values_01['y_16'].mean()
difference_in_mean_02 = df_MediaP2['y_16'].mean() - mean_values_02['y_16'].mean()
difference_in_mean_03 = df_MediaP3['y_16'].mean() - mean_values_03['y_16'].mean()

difference_in_mean_01_per = difference_in_mean_01 / df_MediaP1['y_16'].mean() * 100
difference_in_mean_02_per = difference_in_mean_02 / df_MediaP2['y_16'].mean() * 100
difference_in_mean_03_per = difference_in_mean_03 / df_MediaP3['y_16'].mean() * 100

print(f"Mean Value in DeepMotion 01: {mean_values_01['y_16'].mean()}")
print(f"Mean Value in MediaPipe 01: {df_MediaP1['y_16'].mean()}")
print(f"Difference in Mean: {difference_in_mean_01}")
print(f"Difference in Mean in Percentage: {difference_in_mean_01_per}")
print("")
print(f"Mean Value in DeepMotion 02: {mean_values_02['y_16'].mean()}")
print(f"Mean Value in MediaPipe 02: {df_MediaP2['y_16'].mean()}")
print(f"Difference in Mean: {difference_in_mean_02}")
print(f"Difference in Mean in Percentage: {difference_in_mean_02_per}")
print("")
print(f"Mean Value in DeepMotion 03: {mean_values_03['y_16'].mean()}")
print(f"Mean Value in MediaPipe 03: {df_MediaP3['y_16'].mean()}")
print(f"Difference in Mean: {difference_in_mean_03}")
print(f"Difference in Mean in Percentage: {difference_in_mean_03_per}")

Mean Value in DeepMotion 01: 162.18050183856505
Mean Value in MediaPipe 01: 367.5381621511628
Difference in Mean: 205.35766031259774
Difference in Mean in Percentage: 55.87383337573999

Mean Value in DeepMotion 02: 191.1468976599809
Mean Value in MediaPipe 02: 271.1325231438127
Difference in Mean: 79.9856254838318
Difference in Mean in Percentage: 29.500564726204477

Mean Value in DeepMotion 03: 176.7090342639594
Mean Value in MediaPipe 03: 351.3075072368421
Difference in Mean: 174.59847297288272
Difference in Mean in Percentage: 49.6996134088228

plt.figure(figsize=(12, 6))

plt.plot(mean_values_01.index, mean_values_01["y_16"], color='#054907', linestyle='-', linewidth=2, label='Mean Y_16 DeepMotion 01')
plt.plot(df_MediaP1["frame"], df_MediaP1["y_16"], color='green', linestyle='-', linewidth=2, label='Y_16 Mediapipe 01')

plt.title("y_16 (rechte Hand) Vergleich MEAN DeepMotion und MediaPipe 01")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

df_deepM01.head(5)

# Definition von Kolonnen
columns = ['frame', 'x_0', 'y_0', 'z_0', 'x_11', 'y_11', 'z_11',
           'x_12', 'y_12', 'z_12', 'x_13', 'y_13', 'z_13', 'x_14', 'y_14', 'z_14', 'x_15', 'y_15', 'z_15',
           'x_16', 'y_16', 'z_16', 'x_17', 'y_17', 'z_17', 'x_18', 'y_18', 'z_18', 'x_19', 'y_19', 'z_19',
           'x_20', 'y_20', 'z_20', 'x_21', 'y_21', 'z_21', 'x_22', 'y_22', 'z_22', 'x_23', 'y_23', 'z_23',
           'x_24', 'y_24', 'z_24', 'x_25', 'y_25', 'z_25', 'x_26', 'y_26', 'z_26', 'x_27', 'y_27', 'z_27',
           'x_28', 'y_28', 'z_28', 'x_31', 'y_31', 'z_31', 'x_32', 'y_32', 'z_32']

# Kalkulieren von euclidean Distance
euclidean_distances_01 = {}

for frame in df_MediaP1["frame"]:
    euclidean_distances_01[frame] = []
    for col in columns[1:]:
        point_1 = (frame, mean_values_01[col].loc[frame])
        point_2 = (frame, df_MediaP1[col][df_MediaP1['frame'] == frame].values[0])
        euclidean_distance_01 = distance.euclidean(point_1, point_2)
        euclidean_distances_01[frame].append(euclidean_distance_01)

euclidean_df_01 = pd.DataFrame.from_dict(euclidean_distances_01, orient='index', columns=columns[1:])
euclidean_df_01.head(5)

plt.figure(figsize=(12, 6))

plt.plot(mean_values_01.index, mean_values_01["y_16"], color='#054907', linestyle='-', linewidth=1, label='Mean Y_16 DeepMotion 01')
plt.plot(df_MediaP1["frame"], df_MediaP1["y_16"], color='green', linestyle='-', linewidth=1, label='Y_16 Mediapipe 01')

plt.plot(euclidean_df_01.index, euclidean_df_01["y_16"], color='indigo', linestyle='-', linewidth=1.5, label='Y_16 Euclidean Distances 01')

plt.title("Euclidean Distances: Rechte Hand (y_15) Vergleich Mean DeepMotion und MediaPipe 01")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

mean_distances = euclidean_df_01.mean()
sorted_df = euclidean_df_01[mean_distances.sort_values().index]

plt.figure(figsize=(16, 6))
plt.boxplot(sorted_df.values)
plt.xticks(range(1, len(sorted_df.columns) + 1), sorted_df.columns, rotation=90)
plt.title('Boxplot of Euclidean Distances Sorted by Mean Distance 01')
plt.xlabel('Columns')
plt.ylabel('Euclidean Distance')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

# Definition von Kolonnen
selected_columns = ['x_0', 'x_11', 'x_12', 'x_13', 'x_14', 'x_15', 'x_16', 'x_17', 
                    'x_18', 'x_19', 'x_20', 'x_21', 'x_22', 'x_23', 'x_24', 'y_0', 
                    'y_11', 'y_12', 'y_13', 'y_14', 'y_15', 'y_16', 'y_17', 
                    'y_18', 'y_19', 'y_20', 'y_21', 'y_22', 'y_23', 'y_24']

mean_distances = euclidean_df_01[selected_columns].mean()
sorted_df = euclidean_df_01[mean_distances.sort_values().index]

plt.figure(figsize=(16, 6))
plt.boxplot(sorted_df.values)
plt.xticks(range(1, len(sorted_df.columns) + 1), sorted_df.columns)
plt.title('Boxplot von Euclidean Distances für X- und Y-Werte 01')
plt.ylabel('Euclidean Distance')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

plt.figure(figsize=(12, 6))

plt.plot(mean_values_02.index, mean_values_02["y_16"], color='darkred', linestyle='-', linewidth=2, label='Mean Y_16 DeepMotion 02')
plt.plot(df_MediaP2["frame"], df_MediaP2["y_16"], color='red', linestyle='-', linewidth=2, label='Y_16 Mediapipe 02')

plt.title("Rechte Hand (y_16) Vergleich  Mean DeepMotion und Mediapipe 02")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend( loc='best')
plt.show()

# Definition von Kolonnen
columns = ['frame', 'x_0', 'y_0', 'z_0', 'x_11', 'y_11', 'z_11',
           'x_12', 'y_12', 'z_12', 'x_13', 'y_13', 'z_13', 'x_14', 'y_14', 'z_14', 'x_15', 'y_15', 'z_15',
           'x_16', 'y_16', 'z_16', 'x_17', 'y_17', 'z_17', 'x_18', 'y_18', 'z_18', 'x_19', 'y_19', 'z_19',
           'x_20', 'y_20', 'z_20', 'x_21', 'y_21', 'z_21', 'x_22', 'y_22', 'z_22', 'x_23', 'y_23', 'z_23',
           'x_24', 'y_24', 'z_24', 'x_25', 'y_25', 'z_25', 'x_26', 'y_26', 'z_26', 'x_27', 'y_27', 'z_27',
           'x_28', 'y_28', 'z_28', 'x_31', 'y_31', 'z_31', 'x_32', 'y_32', 'z_32']

# Kalkulieren von euclidean Distance
euclidean_distances_02 = {}

for frame in df_MediaP2["frame"]:
    euclidean_distances_02[frame] = []
    for col in columns[1:]:
        point_1 = (frame, mean_values_02[col].loc[frame])
        point_2 = (frame, df_MediaP2[col][df_MediaP2['frame'] == frame].values[0])
        euclidean_distance_02 = distance.euclidean(point_1, point_2)
        euclidean_distances_02[frame].append(euclidean_distance_02)

euclidean_df_02 = pd.DataFrame.from_dict(euclidean_distances_02, orient='index', columns=columns[1:])
euclidean_df_02.head(5)

plt.figure(figsize=(12, 6))

plt.plot(mean_values_02.index, mean_values_02["y_16"], color='darkred', linestyle='-', linewidth=1, label='Mean Y_16 DeepMotion 02')
plt.plot(df_MediaP2["frame"], df_MediaP2["y_16"], color='red', linestyle='-', linewidth=1, label='Y_16 Mediapipe 02')

plt.plot(euclidean_df_02.index, euclidean_df_02["y_16"], color='indigo', linestyle='-', linewidth=1.5, label='Y_16 Euclidean Distances 02')

plt.title("Euclidean Distances: Rechte Hand (y_16) Vergleich Mean DeepMotion und Mediapipe 02")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend( loc='best')
plt.show()

mean_distances = euclidean_df_02.mean()
sorted_df = euclidean_df_02[mean_distances.sort_values().index]

plt.figure(figsize=(16, 6))
plt.boxplot(sorted_df.values)
plt.xticks(range(1, len(sorted_df.columns) + 1), sorted_df.columns, rotation=90)
plt.title('Boxplot of Euclidean Distances Sorted by Mean Distance 02')
plt.xlabel('Columns')
plt.ylabel('Euclidean Distance')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

# Definition von Kolonnen
selected_columns = ['x_0', 'x_11', 'x_12', 'x_13', 
                    'x_14', 'x_15', 'x_16', 'x_17', 
                    'x_18', 'x_19', 'x_20', 'x_21', 
                    'x_22', 'x_23', 'x_24', 'y_0', 
                    'y_11', 'y_12', 'y_13', 
                    'y_14', 'y_15', 'y_16', 'y_17', 
                    'y_18', 'y_19', 'y_20', 'y_21', 
                    'y_22', 'y_23', 'y_24']

mean_distances = euclidean_df_02[selected_columns].mean()
sorted_df = euclidean_df_02[mean_distances.sort_values().index]

plt.figure(figsize=(16, 6))
plt.boxplot(sorted_df.values)
plt.xticks(range(1, len(sorted_df.columns) + 1), sorted_df.columns)
plt.title('Boxplot of Euclidean Distances für X- und Y-Werte 02')
plt.ylabel('Euclidean Distance')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

plt.figure(figsize=(12, 6))

plt.plot(mean_values_03.index, mean_values_03["y_16"], color='darkblue', linestyle='-', linewidth=2, label='Mean Y_16 DeepMotion 03')
plt.plot(df_MediaP3["frame"], df_MediaP3["y_16"], color='blue', linestyle='-', linewidth=2, label='Y_16 Mediapipe 03')

plt.title("Rechte Hand (y_16) Vergleich Mean DeepMotion und MediaPipe 03")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

# Definition von Kolonnen
columns = ['frame', 'x_0', 'y_0', 'z_0', 'x_11', 'y_11', 'z_11',
           'x_12', 'y_12', 'z_12', 'x_13', 'y_13', 'z_13', 'x_14', 'y_14', 'z_14', 'x_15', 'y_15', 'z_15',
           'x_16', 'y_16', 'z_16', 'x_17', 'y_17', 'z_17', 'x_18', 'y_18', 'z_18', 'x_19', 'y_19', 'z_19',
           'x_20', 'y_20', 'z_20', 'x_21', 'y_21', 'z_21', 'x_22', 'y_22', 'z_22', 'x_23', 'y_23', 'z_23',
           'x_24', 'y_24', 'z_24', 'x_25', 'y_25', 'z_25', 'x_26', 'y_26', 'z_26', 'x_27', 'y_27', 'z_27',
           'x_28', 'y_28', 'z_28', 'x_31', 'y_31', 'z_31', 'x_32', 'y_32', 'z_32']

# Kalkulieren von euclidean Distance
euclidean_distances_03 = {}

for frame in df_MediaP3["frame"]:
    euclidean_distances_03[frame] = []
    for col in columns[1:]:
        point_1 = (frame, mean_values_03[col].loc[frame])
        point_2 = (frame, df_MediaP3[col][df_MediaP3['frame'] == frame].values[0])
        euclidean_distance_03 = distance.euclidean(point_1, point_2)
        euclidean_distances_03[frame].append(euclidean_distance_03)

euclidean_df_03 = pd.DataFrame.from_dict(euclidean_distances_03, orient='index', columns=columns[1:])
euclidean_df_03.head(5)

plt.figure(figsize=(12, 6))

plt.plot(mean_values_03.index, mean_values_03["y_16"], color='darkblue', linestyle='-', linewidth=1, label='Mean Y_16 DeepMotion 03')
plt.plot(df_MediaP3["frame"], df_MediaP3["y_16"], color='blue', linestyle='-', linewidth=1, label='Y_16 Mediapipe 03')

plt.plot(euclidean_df_03.index, euclidean_df_03["y_16"], color='indigo', linestyle='-', linewidth=1.5, label='Y_16 Euclidean Distances 03')

plt.title("Euclidean Distances: Rechte Hand (y_16) Vergleich Mean DeepMotion und MediaPipe 03")
plt.xlabel("Frame")
plt.ylabel("Rechte Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

plt.figure(figsize=(12, 6))

plt.plot(mean_values_03.index, mean_values_03["y_11"], color='#054907', linestyle='-', linewidth=1, label='Mean Y_11 DeepMotion 03')
plt.plot(df_MediaP3["frame"], df_MediaP3["y_11"], color='green', linestyle='-', linewidth=1, label='Y_11 Mediapipe 03')

plt.plot(euclidean_df_03.index, euclidean_df_03["y_11"], color='indigo', linestyle='-', linewidth=1.5, label='Y_11 Euclidean Distances')

plt.title("Euclidean Distances: Linke Hand (y_11) Vergleich Mean DeepMotion und MediaPipe 03")
plt.xlabel("Frame")
plt.ylabel("Linke Hand")
plt.grid(True)
plt.legend(loc='best')
plt.show()

mean_distances = euclidean_df_03.mean()
sorted_df = euclidean_df_03[mean_distances.sort_values().index]

plt.figure(figsize=(16, 6))
plt.boxplot(sorted_df.values)
plt.xticks(range(1, len(sorted_df.columns) + 1), sorted_df.columns, rotation=90)
plt.title('Boxplot of Euclidean Distances Sorted by Mean Distance 03')
plt.xlabel('Columns')
plt.ylabel('Euclidean Distance')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

# Definition von Kolonnen
selected_columns = ['x_0', 'x_11', 'x_12', 'x_13', 
                    'x_14', 'x_15', 'x_16', 'x_17', 
                    'x_18', 'x_19', 'x_20', 'x_21', 
                    'x_22', 'x_23', 'x_24', 'y_0', 
                    'y_11', 'y_12', 'y_13', 
                    'y_14', 'y_15', 'y_16', 'y_17', 
                    'y_18', 'y_19', 'y_20', 'y_21', 
                    'y_22', 'y_23', 'y_24']

mean_distances = euclidean_df_03[selected_columns].mean()
sorted_df = euclidean_df_03[mean_distances.sort_values().index]

plt.figure(figsize=(16, 6))
plt.boxplot(sorted_df.values)
plt.xticks(range(1, len(sorted_df.columns) + 1), sorted_df.columns)
plt.title('Boxplot of Euclidean Distances für X- und Y-Werte 03')
plt.ylabel('Euclidean Distance')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

img = plt.imread('Pictures/landmark.png')
im = OffsetImage(img, zoom=0.2) 
ab = AnnotationBbox(im, (0.5, 0.5), frameon=False)
plt.gca().add_artist(ab)
plt.axis('off')
plt.show()

img = plt.imread('Pictures/ausgangsposition.jpg')
im = OffsetImage(img, zoom=0.2)
ab = AnnotationBbox(im, (0.5, 0.5), frameon=False)
plt.gca().add_artist(ab)
plt.axis('off')
plt.show()

img = plt.imread('Pictures/bewegung.jpg')
im = OffsetImage(img, zoom=0.2)
ab = AnnotationBbox(im, (0.5, 0.5), frameon=False)
plt.gca().add_artist(ab)

plt.axis('off')
plt.show()

img = plt.imread('Pictures/komp_1.jpg')
im = OffsetImage(img, zoom=0.4)
ab = AnnotationBbox(im, (0.5, 0.5), frameon=False)
plt.gca().add_artist(ab)

plt.axis('off')
plt.show()

img = plt.imread('Pictures/komp2.jpg')
im = OffsetImage(img, zoom=0.2)
ab = AnnotationBbox(im, (0.5, 0.5), frameon=False)
plt.gca().add_artist(ab)

plt.axis('off')
plt.show()

img = plt.imread('Pictures/x_2.jpg')
im = OffsetImage(img, zoom=0.4)
ab = AnnotationBbox(im, (0.5, 0.5), frameon=False)
plt.gca().add_artist(ab)

plt.axis('off')
plt.show()

img = plt.imread('Pictures/x_3.jpg')
im = OffsetImage(img, zoom=0.6)
ab = AnnotationBbox(im, (0.5, 0.5), frameon=False)
plt.gca().add_artist(ab)

plt.axis('off')
plt.show()

selected_columns = ['x_0', 'y_0', 'z_0', 'x_11', 'y_11', 'z_11',
                    'x_12', 'y_12', 'z_12', 'x_13', 'y_13', 'z_13', 
                    'x_14', 'y_14', 'z_14', 'x_15', 'y_15', 'z_15',
                    'x_16', 'y_16', 'z_16', 'x_17', 'y_17', 'z_17', 
                    'x_18', 'y_18', 'z_18', 'x_19', 'y_19', 'z_19',
                    'x_20', 'y_20', 'z_20', 'x_21', 'y_21', 'z_21', 
                    'x_22', 'y_22', 'z_22', 'x_23', 'y_23', 'z_23',
                    'x_24', 'y_24', 'z_24']

difference_in_mean_upper_body_01 = df_MediaP1[selected_columns].mean() - mean_values_01[selected_columns].mean()
print(f"Mean Value in DeepMotion 01: {difference_in_mean_upper_body_01}")

Mean Value in DeepMotion 01: x_0      -3.319083
y_0      73.683774
z_0    -210.642338
x_11    116.795024
y_11    146.385612
z_11    374.395101
x_12   -149.673985
y_12    104.762081
z_12    374.873850
x_13     11.958495
y_13    257.475238
z_13    353.311798
x_14   -162.232370
y_14    183.706348
z_14    133.704332
x_15     21.795657
y_15    245.967297
z_15    -91.969699
x_16   -113.446733
y_16    205.357660
z_16   -389.343679
x_17     16.749902
y_17    238.363306
z_17   -211.062132
x_18    -94.916984
y_18    212.191708
z_18   -524.594197
x_19     23.633374
y_19    226.420394
z_19   -202.265748
x_20    -87.188510
y_20    200.584724
z_20   -479.274081
x_21     11.851888
y_21    223.093311
z_21    -99.649882
x_22    -76.151072
y_22    188.579113
z_22   -382.626055
x_23    -19.884797
y_23    183.582298
z_23    957.603535
x_24   -105.803264
y_24    164.761015
z_24    970.039118
dtype: float64

selected_columns_X = ['x_0', 'x_11', 'x_12', 'x_13', 
                    'x_14', 'x_15', 'x_16', 'x_17', 
                    'x_18', 'x_19', 'x_20', 'x_21', 
                    'x_22', 'x_23', 'x_24']

selected_columns_Y = ['y_0', 'y_11', 'y_12', 'y_13', 
                    'y_14', 'y_15', 'y_16', 'y_17', 
                    'y_18', 'y_19', 'y_20', 'y_21', 
                    'y_22', 'y_23', 'y_24']

selected_columns_Z = ['z_0', 'z_11', 'z_12', 'z_13', 
                    'z_14', 'z_15', 'z_16', 'z_17', 
                    'z_18', 'z_19', 'z_20', 'z_21', 
                    'z_22', 'z_23', 'z_24']

difference_in_mean_upper_body_01_X = difference_in_mean_upper_body_01[selected_columns_X]
difference_in_mean_upper_body_01_Y = difference_in_mean_upper_body_01[selected_columns_Y]
difference_in_mean_upper_body_01_Z = difference_in_mean_upper_body_01[selected_columns_Z]

plt.figure(figsize=(12, 8))
plt.bar(difference_in_mean_upper_body_01_X.index, difference_in_mean_upper_body_01_X, color='skyblue')
plt.title('Differences in Mean for Upper Body')
plt.xlabel('Landmarks')
plt.ylabel('Difference')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

plt.figure(figsize=(12, 8))
plt.bar(difference_in_mean_upper_body_01_Y.index, difference_in_mean_upper_body_01_Y, color='skyblue')
plt.title('Differences in Mean for Upper Body')
plt.xlabel('Landmarks')
plt.ylabel('Difference')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

plt.figure(figsize=(12, 8))
plt.bar(difference_in_mean_upper_body_01_Z.index, difference_in_mean_upper_body_01_Z, color='skyblue')
plt.title('Differences in Mean for Upper Body')
plt.xlabel('Landmarks')
plt.ylabel('Difference')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

difference_in_mean_upper_body_02 = df_MediaP2[selected_columns].mean() - mean_values_02[selected_columns].mean()
print(f"Mean Value in DeepMotion 02: {difference_in_mean_upper_body_02}")

Mean Value in DeepMotion 02: x_0     150.901215
y_0      45.595955
z_0    -227.528791
x_11    283.252751
y_11     61.692810
z_11    285.009399
x_12     46.207360
y_12     15.589027
z_12    262.641740
x_13    197.326623
y_13    102.834697
z_13    315.100115
x_14     65.072355
y_14     74.540943
z_14    132.690039
x_15    222.777897
y_15    126.753305
z_15   -184.942157
x_16     80.540867
y_16     79.985625
z_16   -418.256537
x_17    212.372087
y_17    155.749507
z_17   -296.386476
x_18     92.287134
y_18    103.080864
z_18   -534.293733
x_19    222.254899
y_19    158.058629
z_19   -341.451427
x_20     92.491910
y_20     99.587247
z_20   -536.114006
x_21    221.153078
y_21    136.507000
z_21   -220.009200
x_22     97.359988
y_22     83.443998
z_22   -434.171967
x_23    167.449638
y_23    204.225627
z_23    529.435083
x_24     91.554630
y_24    196.521203
z_24    480.946851
dtype: float64

difference_in_mean_upper_body_02_X = difference_in_mean_upper_body_02[selected_columns_X]
difference_in_mean_upper_body_02_Y = difference_in_mean_upper_body_02[selected_columns_Y]
difference_in_mean_upper_body_02_Z = difference_in_mean_upper_body_02[selected_columns_Z]

plt.figure(figsize=(12, 8))
plt.bar(difference_in_mean_upper_body_02_X.index, difference_in_mean_upper_body_02_X, color='skyblue')
plt.title('Differences in Mean for Upper Body')
plt.xlabel('Landmarks')
plt.ylabel('Difference')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

plt.figure(figsize=(12, 8))
plt.bar(difference_in_mean_upper_body_02_Y.index, difference_in_mean_upper_body_02_Y, color='skyblue')
plt.title('Differences in Mean for Upper Body')
plt.xlabel('Landmarks')
plt.ylabel('Difference')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

plt.figure(figsize=(12, 8))
plt.bar(difference_in_mean_upper_body_02_Z.index, difference_in_mean_upper_body_02_Z, color='skyblue')
plt.title('Differences in Mean for Upper Body')
plt.xlabel('Landmarks')
plt.ylabel('Difference')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

difference_in_mean_upper_body_03 = df_MediaP3[selected_columns].mean() - mean_values_03[selected_columns].mean()
print(f"Mean Value in DeepMotion 03: {difference_in_mean_upper_body_03}")

Mean Value in DeepMotion 03: x_0     -79.727432
y_0     -12.878601
z_0    -215.486841
x_11     40.958609
y_11     96.163076
z_11    421.896135
x_12   -219.902243
y_12     81.928650
z_12    427.840876
x_13    -13.864860
y_13    199.769663
z_13    411.117255
x_14   -198.952793
y_14    176.548532
z_14    145.939174
x_15    -12.935756
y_15    265.261322
z_15   -157.637703
x_16   -149.607376
y_16    174.598473
z_16   -581.207535
x_17    -16.636216
y_17    298.852667
z_17   -310.012186
x_18   -134.422691
y_18    188.485895
z_18   -749.404108
x_19    -19.505289
y_19    286.740996
z_19   -335.861554
x_20   -138.045069
y_20    176.511838
z_20   -732.746303
x_21    -25.007999
y_21    268.128056
z_21   -187.053607
x_22   -134.235969
y_22    157.867234
z_22   -592.268149
x_23    -42.196064
y_23    270.904955
z_23    667.807884
x_24   -147.509207
y_24    273.400205
z_24    721.503507
dtype: float64

difference_in_mean_upper_body_03_X = difference_in_mean_upper_body_03[selected_columns_X]
difference_in_mean_upper_body_03_Y = difference_in_mean_upper_body_03[selected_columns_Y]
difference_in_mean_upper_body_03_Z = difference_in_mean_upper_body_03[selected_columns_Z]

plt.figure(figsize=(12, 8))
plt.bar(difference_in_mean_upper_body_03_X.index, difference_in_mean_upper_body_03_X, color='skyblue')
plt.title('Differences in Mean for Upper Body')
plt.xlabel('Landmarks')
plt.ylabel('Difference')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

plt.figure(figsize=(12, 8))
plt.bar(difference_in_mean_upper_body_03_Y.index, difference_in_mean_upper_body_03_Y, color='skyblue')
plt.title('Differences in Mean for Upper Body')
plt.xlabel('Landmarks')
plt.ylabel('Difference')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

plt.figure(figsize=(12, 8))
plt.bar(difference_in_mean_upper_body_03_Z.index, difference_in_mean_upper_body_03_Z, color='skyblue')
plt.title('Differences in Mean for Upper Body')
plt.xlabel('Landmarks')
plt.ylabel('Difference')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

	frame	path	x_0	x_1	x_2	x_3	x_4	x_5	x_6	...	z_23	z_24	z_25	z_26	z_27	z_28	z_29	z_30	z_31	z_32
0	1	/Users/salomekoller/Library/CloudStorage/OneDr...	0.0000	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	...	-54.873562	-54.499969	-85.17718	-63.896238	-139.38191	-138.26741	-60.97508	-60.97508	-93.70720	-92.42378
1	2	/Users/salomekoller/Library/CloudStorage/OneDr...	-0.2305	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	...	-52.931379	-52.670462	-84.30306	-62.929292	-139.78187	-138.25643	-60.97508	-60.97508	-94.39583	-92.64646
2	3	/Users/salomekoller/Library/CloudStorage/OneDr...	-0.5451	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	...	-51.806282	-51.619093	-83.84154	-62.306914	-139.79828	-138.27923	-60.97508	-60.97508	-94.53404	-92.81575
3	4	/Users/salomekoller/Library/CloudStorage/OneDr...	-0.8911	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	...	-50.842950	-50.702760	-83.26549	-61.552639	-139.34388	-137.97698	-60.97508	-60.97508	-94.12387	-92.60972
4	5	/Users/salomekoller/Library/CloudStorage/OneDr...	-1.2253	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	-993.7168	...	-49.747330	-49.598590	-82.18803	-60.119033	-137.88579	-136.66514	-60.97508	-60.97508	-92.57480	-91.30198

	x_0	y_0	z_0	x_1	y_1	z_1	x_2	y_2	z_2	x_3	...	z_29	x_30	y_30	z_30	x_31	y_31	z_31	x_32	y_32	z_32
frame
1	65.99356	18.27286	-171.467506	-993.7168	-312.4525	-60.97508	-993.7168	-312.4525	-60.97508	-993.7168	...	-60.97508	-993.7168	-312.4525	-60.97508	154.4078	805.5755	-255.696504	72.54712	806.6257	-255.794900
2	66.85646	19.80700	-171.254204	-993.7168	-312.4525	-60.97508	-993.7168	-312.4525	-60.97508	-993.7168	...	-60.97508	-993.7168	-312.4525	-60.97508	154.3102	807.3189	-255.160410	72.41398	808.1503	-255.459556
3	67.98854	20.18948	-170.908414	-993.7168	-312.4525	-60.97508	-993.7168	-312.4525	-60.97508	-993.7168	...	-60.97508	-993.7168	-312.4525	-60.97508	154.0066	807.8827	-254.986212	72.10524	808.3263	-255.291174
4	68.69194	19.89282	-170.505374	-993.7168	-312.4525	-60.97508	-993.7168	-312.4525	-60.97508	-993.7168	...	-60.97508	-993.7168	-312.4525	-60.97508	154.3780	807.6317	-255.097998	72.50384	807.9017	-256.291508
5	69.62770	19.35010	-170.065084	-993.7168	-312.4525	-60.97508	-993.7168	-312.4525	-60.97508	-993.7168	...	-60.97508	-993.7168	-312.4525	-60.97508	154.3640	807.1665	-255.087324	72.51816	807.2171	-256.741820

	compensation	frame	path	x_0	x_1	x_2	x_3	x_4	x_5	x_6	...	z_23	z_24	z_25	z_26	z_27	z_28	z_29	z_30	z_31	z_32
0	1	1	/Users/salomekoller/Library/CloudStorage/OneDr...	0.000	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	...	-54.232452	-55.732347	-86.36791	-73.35297	-140.74894	-141.03328	-48.49379	-48.49379	-95.50346	-95.37153
1	1	2	/Users/salomekoller/Library/CloudStorage/OneDr...	1.909	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	...	-55.343797	-56.752106	-86.88588	-74.65325	-140.99239	-141.31013	-48.49379	-48.49379	-95.63772	-95.59663
2	1	3	/Users/salomekoller/Library/CloudStorage/OneDr...	2.832	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	...	-55.947689	-57.295671	-87.15947	-75.24743	-140.94134	-141.37022	-48.49379	-48.49379	-95.49199	-95.61985
3	1	4	/Users/salomekoller/Library/CloudStorage/OneDr...	3.748	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	...	-56.572451	-57.857934	-87.47403	-75.87163	-140.93407	-141.53690	-48.49379	-48.49379	-95.39195	-95.76678
4	1	5	/Users/salomekoller/Library/CloudStorage/OneDr...	5.316	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	-1007.957	...	-57.970961	-59.166960	-88.48003	-77.44732	-141.46560	-142.56488	-48.49379	-48.49379	-95.79207	-96.77919

	x_0	y_0	z_0	x_1	y_1	z_1	x_2	y_2	z_2	x_3	...	z_29	x_30	y_30	z_30	x_31	y_31	z_31	x_32	y_32	z_32
frame
1	42.9238	16.61496	-200.431752	-1007.957	-310.477	-48.49379	-1007.957	-310.477	-48.49379	-1007.957	...	-48.49379	-1007.957	-310.477	-48.49379	108.7978	804.2580	-310.868084	27.40192	805.4124	-303.746878
2	44.8634	16.47286	-202.449376	-1007.957	-310.477	-48.49379	-1007.957	-310.477	-48.49379	-1007.957	...	-48.49379	-1007.957	-310.477	-48.49379	108.1234	804.7362	-310.319296	26.74318	805.4902	-303.127498
3	46.3038	16.14494	-204.223192	-1007.957	-310.477	-48.49379	-1007.957	-310.477	-48.49379	-1007.957	...	-48.49379	-1007.957	-310.477	-48.49379	108.0088	804.9022	-309.836790	26.64796	805.4070	-302.555662
4	47.5600	15.77780	-206.149102	-1007.957	-310.477	-48.49379	-1007.957	-310.477	-48.49379	-1007.957	...	-48.49379	-1007.957	-310.477	-48.49379	108.8146	804.7238	-311.051902	27.48648	805.1952	-303.530168
5	49.5454	15.59410	-208.155064	-1007.957	-310.477	-48.49379	-1007.957	-310.477	-48.49379	-1007.957	...	-48.49379	-1007.957	-310.477	-48.49379	108.6924	805.0104	-311.115766	27.35456	805.2414	-303.624450

	compensation	frame	path	x_0	x_1	x_2	x_3	x_4	x_5	x_6	...	z_23	z_24	z_25	z_26	z_27	z_28	z_29	z_30	z_31	z_32
0	1	1	/Users/salomekoller/Library/CloudStorage/OneDr...	0.000	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	...	-37.69084	-28.31890	-38.89564	-45.227116	-101.69009	-89.82048	-53.49067	-53.49067	-53.797332	-41.81292
1	1	2	/Users/salomekoller/Library/CloudStorage/OneDr...	5.366	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	...	-33.65205	-24.53783	-35.95330	-44.349021	-101.89741	-89.62951	-53.49067	-53.49067	-54.520770	-42.03391
2	1	3	/Users/salomekoller/Library/CloudStorage/OneDr...	8.136	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	...	-31.50526	-22.53382	-34.32710	-43.744561	-101.77919	-89.61607	-53.49067	-53.49067	-54.623282	-42.25433
3	1	4	/Users/salomekoller/Library/CloudStorage/OneDr...	13.775	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	...	-26.93351	-18.24678	-30.77586	-42.135530	-100.91355	-89.55945	-53.49067	-53.49067	-54.088738	-42.71065
4	1	5	/Users/salomekoller/Library/CloudStorage/OneDr...	15.967	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	-1045.738	...	-24.44174	-15.80145	-28.45445	-40.110030	-99.01383	-87.68407	-53.49067	-53.49067	-52.258401	-40.89253

	x_0	y_0	z_0	x_1	y_1	z_1	x_2	y_2	z_2	x_3	...	z_29	x_30	y_30	z_30	x_31	y_31	z_31	x_32	y_32	z_32
frame
1	-80.50884	8.62712	-102.583294	-1045.738	-307.2164	-53.49067	-1045.738	-307.2164	-53.49067	-1045.738	...	-53.49067	-1045.738	-307.2164	-53.49067	-26.06254	771.53252	-85.387361	-208.51494	781.1454	-110.230288
2	-77.81076	8.94612	-98.155338	-1045.738	-307.2164	-53.49067	-1045.738	-307.2164	-53.49067	-1045.738	...	-53.49067	-1045.738	-307.2164	-53.49067	-26.57306	772.13808	-85.759563	-208.38156	780.9080	-110.333784
3	-75.21866	9.22386	-93.563912	-1045.738	-307.2164	-53.49067	-1045.738	-307.2164	-53.49067	-1045.738	...	-53.49067	-1045.738	-307.2164	-53.49067	-27.16772	772.94482	-86.051667	-208.08650	780.7230	-110.410921
4	-72.21568	9.81578	-88.551916	-1045.738	-307.2164	-53.49067	-1045.738	-307.2164	-53.49067	-1045.738	...	-53.49067	-1045.738	-307.2164	-53.49067	-27.28626	772.99346	-86.090571	-208.09140	780.6738	-110.657255
5	-69.32478	10.38222	-82.843140	-1045.738	-307.2164	-53.49067	-1045.738	-307.2164	-53.49067	-1045.738	...	-53.49067	-1045.738	-307.2164	-53.49067	-26.90568	773.14880	-85.860668	-207.84652	780.6538	-110.558228

	path	frame	compensation	x_0	y_0	z_0	x_1	y_1	z_1	x_2	...	z_29	x_30	y_30	z_30	x_31	y_31	z_31	x_32	y_32	z_32
0	bachelorarbeiten/nils/data/videos/samples/drin...	1	False	0.00000	0.00000	0.00000	20.93640	-23.58810	67.128800	32.30596	...	28.99000	-111.31410	500.86573	212.30550	149.0010	443.31818	-183.80580	-106.19807	468.39473	35.82680
1	bachelorarbeiten/nils/data/videos/samples/drin...	2	False	-0.08766	0.51810	61.53380	20.51320	-23.60074	137.915300	31.65790	...	144.98176	-117.10200	628.15216	390.75823	107.9158	509.11103	-100.04770	-116.11866	605.60577	130.99010
2	bachelorarbeiten/nils/data/videos/samples/drin...	3	False	-0.81886	0.99103	143.05466	19.41430	-23.59127	218.420940	30.26354	...	130.40780	-123.36020	642.35313	494.83884	86.0822	602.66028	-130.16710	-120.94795	653.28283	192.77330
3	bachelorarbeiten/nils/data/videos/samples/drin...	4	False	-1.46955	1.41806	349.09104	18.92890	-23.51630	419.324940	29.69970	...	346.76520	-123.25700	662.83948	1253.09060	101.4250	643.70493	54.78520	-118.50544	716.12153	986.52970
4	bachelorarbeiten/nils/data/videos/samples/drin...	5	False	-2.07000	1.49976	450.38204	18.47704	-23.52460	517.691055	29.29530	...	-31.00470	-117.32246	701.49013	-67.27220	74.0153	634.92139	-238.75634	-111.79520	682.78198	-297.20605

	x_0	y_0	z_0	x_1	y_1	z_1	x_2	y_2	z_2	x_3	...	z_29	x_30	y_30	z_30	x_31	y_31	z_31	x_32	y_32	z_32
frame
1	0.000000	0.000000	0.000000	19.160885	-26.792770	61.691245	29.972915	-26.719330	61.493435	39.138005	...	209.402607	-107.189030	711.185805	256.892687	87.509185	691.663680	-1.200680	-130.429570	733.201405	38.699470
2	0.024455	1.189490	31.137870	19.269535	-26.128870	99.299155	29.928935	-26.037695	99.027580	39.343370	...	249.714590	-108.380265	773.733720	324.807150	64.899685	721.727105	12.367835	-129.580445	801.406425	52.532025
3	-0.310065	2.638355	55.155305	18.940485	-24.881945	122.820275	29.419055	-24.742775	122.541585	38.976435	...	238.095255	-110.537415	780.105255	375.034510	52.445935	767.600630	-7.954615	-129.464420	825.077505	82.604545
4	0.255890	3.420960	150.906590	19.777185	-24.170795	216.327370	30.183885	-24.029625	216.077535	39.729435	...	346.054017	-110.235465	790.696580	743.221905	59.990435	787.048805	83.039525	-128.408635	857.127605	462.616185
5	0.683905	4.665790	185.844140	20.488755	-22.907115	249.370338	30.919985	-22.769595	249.065295	40.560115	...	159.070830	-107.227495	809.260605	88.999010	46.351885	780.251085	-58.020870	-125.150985	840.124980	-167.470580

	x_0	y_0	z_0	x_11	y_11	z_11	x_12	y_12	z_12	x_13	...	z_27	x_28	y_28	z_28	x_31	y_31	z_31	x_32	y_32	z_32
1	42.92380	16.61496	200.431752	69.3176	90.13566	758.638164	168.83520	74.98150	659.808978	10.8751	...	410.550930	140.81896	285.66887	557.936898	40.2032	360.93982	127.062284	133.59999	337.01767	339.573678
2	44.95106	15.95476	263.983176	69.9716	89.87085	831.607521	171.16664	76.69971	806.748098	24.7191	...	524.462780	143.35470	163.73097	755.557618	0.2076	295.62517	210.271596	142.86184	199.88443	434.117598
3	47.12266	15.15391	347.277852	70.6990	91.24217	862.063893	173.41177	78.77599	878.335758	29.4000	...	492.755260	148.24251	138.85617	840.397456	21.9266	202.24192	179.669690	147.59591	152.12417	495.328962
4	49.02955	14.35974	555.240142	69.6152	91.58547	1001.308958	175.43172	80.24593	1026.844498	30.8039	...	684.222126	147.70005	104.16461	1560.973172	7.3896	161.01887	365.837102	145.99192	89.07367	1290.059868
5	51.61540	14.09434	658.537104	70.5362	92.12765	1103.323308	177.55982	81.06427	1107.191584	28.8831	...	365.779952	144.68048	92.63177	324.185228	34.6771	170.08901	72.359426	139.14976	122.45942	6.418400

	x_0	y_0	z_0	x_11	y_11	z_11	x_12	y_12	z_12	x_13	...	z_27	x_28	y_28	z_28	x_31	y_31	z_31	x_32	y_32	z_32
1	80.50884	8.62712	102.583294	204.19991	75.73850	612.477154	33.43345	38.33725	603.094474	150.98677	...	467.131492	62.47735	126.82504	413.357570	52.07991	168.47666	266.791801	53.85387	216.86268	151.802428
2	77.94733	7.08524	98.897278	202.00413	75.67390	621.639808	34.80006	39.15704	605.273147	153.93395	...	429.987844	70.76138	146.65892	365.403114	48.45663	162.20510	210.542933	65.33933	216.29908	84.407734
3	75.41739	4.93818	60.819862	199.83341	74.17957	609.463960	36.16155	39.78898	590.131785	154.25215	...	422.519454	74.54285	153.74438	362.500270	45.97739	159.59616	200.309537	70.10561	216.14918	82.846711
4	74.19701	4.39192	41.274056	197.32713	72.01682	591.316945	37.25993	40.15608	572.235943	151.83201	...	422.745214	75.23707	160.14372	343.524672	45.84213	157.39922	197.384421	69.77957	217.45988	49.359925
5	72.76259	2.55040	4.149380	195.28057	69.82888	569.920596	37.51685	40.32108	549.975651	150.62297	...	424.255892	75.12673	158.61992	355.788026	45.59415	152.43198	208.575268	69.33975	216.81418	72.823118

	x_0	y_0	z_0	x_11	y_11	z_11	x_12	y_12	z_12	x_13	...	z_27	x_28	y_28	z_28	x_31	y_31	z_31	x_32	y_32	z_32
1	65.99356	18.27286	171.467506	49.6439	99.93474	668.763334	205.30202	74.48721	688.282391	0.7459	...	413.052336	225.08979	8.99114	1231.108256	99.1440	27.38124	104.787014	248.68692	8.48936	973.747530
2	67.48476	19.79484	130.008146	48.9014	95.24961	431.186842	206.08323	75.83324	440.554680	2.6915	...	1435.074708	205.41174	192.81236	1900.078300	79.3313	236.46426	1119.853360	219.20268	227.20806	1584.072346
3	68.77944	20.10476	121.484736	48.5285	93.40679	434.953544	207.17242	78.70648	481.038302	4.2624	...	1514.271030	202.19752	231.88596	1928.342220	81.6821	289.53926	1210.362002	208.15234	268.37936	1617.334324
4	70.54364	19.92736	2.222716	49.3687	93.34960	490.740326	208.43588	80.55566	555.391135	5.0053	...	1645.335350	192.36276	259.18446	1888.054050	114.2689	311.15276	1365.978848	185.98669	298.26186	1590.564258
5	71.39090	19.31311	24.813634	49.5948	93.57797	519.740428	209.44843	83.20356	578.571426	6.1327	...	1517.501582	189.90474	261.74046	1807.583922	112.8796	313.48996	1245.937974	183.33859	300.14266	1524.639030

	path	frame	compensation	x_0	y_0	z_0	x_1	y_1	z_1	x_2	...	z_29	x_30	y_30	z_30	x_31	y_31	z_31	x_32	y_32	z_32
172	bachelorarbeiten/nils/data/videos/samples/03_t...	1	False	0.00000	0.00000	0.00000	17.38537	-29.99744	56.25369	27.63987	...	389.815213	-103.06396	921.50588	301.479875	26.01737	940.00918	181.40444	-154.66107	998.00808	41.57214
173	bachelorarbeiten/nils/data/videos/samples/03_t...	2	False	0.13657	1.86088	0.74194	18.02587	-28.65700	60.68301	28.19997	...	354.447420	-99.65853	919.31528	258.856070	21.88357	934.34318	124.78337	-143.04223	997.20708	-25.92605
174	bachelorarbeiten/nils/data/videos/samples/03_t...	3	False	0.19873	4.28568	-32.74405	18.46667	-26.17262	27.21961	28.57457	...	345.782710	-97.71463	917.85738	255.230180	18.80967	932.54098	114.25787	-137.98089	996.87218	-27.56421
175	bachelorarbeiten/nils/data/videos/samples/03_t...	4	False	1.98133	5.42386	-47.27786	20.62547	-24.82529	13.32980	30.66807	...	345.342835	-97.21393	918.55368	233.353210	18.55587	930.39268	111.29385	-138.31183	998.13368	-61.29733
176	bachelorarbeiten/nils/data/videos/samples/03_t...	5	False	3.43781	7.83182	-78.69376	22.50047	-22.28963	-18.95038	32.54467	...	349.146360	-97.13253	917.03108	245.270220	18.68847	925.58078	122.71460	-138.50677	997.46798	-37.73511

	path	frame	compensation	x_0	y_0	z_0	x_1	y_1	z_1	x_2	...	z_29	x_30	y_30	z_30	x_31	y_31	z_31	x_32	y_32	z_32
471	bachelorarbeiten/nils/data/videos/samples/drin...	1	False	0.0000	0.00000	0.00000	22.24680	-24.15484	65.92985	34.8689	...	145.00479	-147.09710	771.05986	982.87658	55.2638	778.19426	-150.90949	-176.13980	815.11506	717.95263
472	bachelorarbeiten/nils/data/videos/samples/drin...	2	False	-0.6283	0.01216	-301.26235	21.56970	-24.26059	-240.94045	33.8579	...	1194.78405	-135.65660	993.49076	1673.93625	74.9789	1043.78316	864.69295	-146.78870	1035.35836	1328.61279
473	bachelorarbeiten/nils/data/videos/samples/drin...	3	False	-0.7909	0.08472	-292.39315	21.42860	-24.49930	-233.72395	33.4548	...	1278.60760	-135.40540	1035.81876	1704.44805	72.3245	1097.42196	955.37579	-136.04710	1076.70566	1362.04315
474	bachelorarbeiten/nils/data/videos/samples/drin...	4	False	-1.8517	-0.03454	-172.72809	20.84754	-25.05017	-114.56159	32.8417	...	1410.71525	-128.04266	1065.27046	1651.87315	40.1091	1118.78446	1110.88085	-113.48285	1106.16356	1334.27275
475	bachelorarbeiten/nils/data/videos/samples/drin...	5	False	-1.7632	0.03699	-145.25145	20.90534	-25.24734	-90.44129	32.8452	...	1279.34845	-126.30810	1065.57436	1570.90455	41.4844	1120.65646	990.85065	-110.82043	1107.35976	1267.89721

Datenanalyse MediaPipe und DeepMotion¶

1. DeepMotion Nicht Kompensiert 03¶

1.1 Analyse Form¶

1.2 Splitting Frames¶

1.3 Bewegungsanalyse anhand rechter Handbewegung (Nicht Kompensiert 03)¶

1.3.1 Berechnung des Mean¶

1.3.2 Bewegungsmuster mit Mean¶

2. DeepMotion Kompensiert 01¶

2.1 Analyse Form¶

2.2 Splitting Frames¶

2.3 Bewegungsanalyse anhand rechter Hand (Kompensiert 01)¶

2.3.1 Berechnung des Mean¶

2.4 Bewegungsanalyse linker Ellbogen (Kompensiert 01)¶

2.5 Bewegungsanalyse linke Hand (Kompensiert 01)¶

3. DeepMotion Kompensiert 02¶

3.1 Analyse Form¶

3.2 Splitting Frames¶

3.3 Bewegungsanalyse anhand rechter Handbewegung (Kompensiert 02)¶

3.3.1 Berechnung des Mean¶

3.3.2 Bewegungsmuster mit Mean¶

4. MediaPipe¶

4.1 Analyse form¶

4.2 Splitting Frames¶

4.3.1 Vergleich Länge Mediapipe Frame zu DeepMotion Frame 01¶

4.3.2 Vergleich Länge Mediapipe Frame zu DeepMotion Frame 02¶

4.3.3 Vergleich Länge Mediapipe Frame zu DeepMotion Frame 03¶

4.4 Rechter Arm (z_16) Bewegung Mediapipe¶

4.5 Kopfbewegung (z_0) Mediapipe¶

4.6 Bewegungsanalyse anhand rechter Handbewegung¶

Berechnung Euclidean Distances¶

1. Analyse der Mean Werte¶

2. Vergleich Werte MediaPipe und DeepMotion¶

2.1 Kompensiert 01¶

2.1.1 Euclidean Distances¶

2.1.1.1 Ohne Z Werte¶

2.2 Kompensiert 02¶

2.2.1 Euclidean Distances¶

2.2.1.1 Ohne Z Werte¶

2.3 Nicht Kompensiert 03¶

2.3.1 Euclidean Distances¶

2.3.1.1 Ohne Z Werte¶

Erkenntnisse¶

3. Vergleich ganzer Oberkörper¶

4. Difference in Mean Upper Body¶

4.1 Kompensation 01¶

4.2 Kompensation 02¶

4.3 Nicht Kompensiert 03¶